from llama_index.core import SimpleDirectoryReader
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader

from llama_index.core import SimpleDirectoryReader
from llama_index.core.node_parser import SimpleNodeParser
from llama_index.core import  GPTVectorStoreIndex,VectorStoreIndex
from llama_index.llms import openai_like
from llama_index.core import Settings
from llama_index.llms.ollama import Ollama
from llama_index.embeddings.huggingface import HuggingFaceEmbedding  # HuggingFaceEmbedding:用于将文本转换为词向量
from llama_index.llms.huggingface import HuggingFaceLLM  # HuggingFaceLLM：用于运行Hugging Face的预训练语言模型
from llama_index.core import Settings,SimpleDirectoryReader,VectorStoreIndex
import chromadb
from llama_index.embeddings.dashscope import DashScopeEmbedding
from llama_index.vector_stores.chroma import ChromaVectorStore
from llama_index.core import StorageContext, load_index_from_storage
from llama_index.llms.deepseek  import DeepSeek
from llama_index.embeddings.fastembed import FastEmbedEmbedding

from llama_index.core import QueryBundle

# import NodeWithScore
from llama_index.core.schema import NodeWithScore

# Retrievers
from llama_index.core.retrievers import (
    BaseRetriever,
    VectorIndexRetriever,
    KeywordTableSimpleRetriever,
)
    # 连接Chroma数据库


llm = DeepSeek(model="deepseek-chat", api_key="sk-605e60a1301040759a821b6b677556fb")
Settings.llm = llm
embed_model = FastEmbedEmbedding(model_name="BAAI/bge-small-en-v1.5")
Settings.embed_model=embed_model
 

 # load documents
documents = SimpleDirectoryReader("./data//paul_graham/").load_data()

index = VectorStoreIndex.from_documents(documents)
retriever = index.as_retriever()


from llama_index.llms.openai import OpenAI
from llama_index.core import Settings

 

nodes = Settings.node_parser.get_nodes_from_documents(documents)

from llama_index.core import StorageContext

# initialize storage context (by default it's in-memory)
storage_context = StorageContext.from_defaults()
storage_context.docstore.add_documents(nodes)

from llama_index.core import SimpleKeywordTableIndex, VectorStoreIndex

keyword_index = SimpleKeywordTableIndex(
    nodes,
    storage_context=storage_context,
    show_progress=True,
)
vector_index = VectorStoreIndex(
    nodes,
    storage_context=storage_context,
    show_progress=True,
)

from llama_index.core import PromptTemplate

QA_PROMPT_TMPL = (
    "Context information is below.\n"
    "---------------------\n"
    "{context_str}\n"
    "---------------------\n"
    "Given the context information and not prior knowledge, "
    "answer the question. If the answer is not in the context, inform "
    "the user that you can't answer the question - DO NOT MAKE UP AN ANSWER.\n"
    "In addition to returning the answer, also return a relevance score as to "
    "how relevant the answer is to the question. "
    "Question: {query_str}\n"
    "Answer (including relevance score): "
)
QA_PROMPT = PromptTemplate(QA_PROMPT_TMPL)

keyword_query_engine = keyword_index.as_query_engine(
    text_qa_template=QA_PROMPT
)
vector_query_engine = vector_index.as_query_engine(text_qa_template=QA_PROMPT)

from llama_index.core.composability import QASummaryQueryEngineBuilder

query_engine_builder = QASummaryQueryEngineBuilder(
    llm=llm,
)
query_engine = query_engine_builder.build_from_documents(documents)

output=query_engine.query("who am i ")
print(output)